{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats_conversion.08-08-2025.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 8 Aug 2025, 14:56:46
{txt}
{com}. 
. 
. 
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. 
. *** BPSUMSTATS CONVERSION TO STATA -- VERSION 6 STATISTICAL ANALYSIS OF "ORGANIZATIONAL PERFORMANCE" PROJECT [KRAUSE & LEWIS] 08-08-2025 ****
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******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
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. **** STEP 1: OPEN EACH "BPSUMSTATS" EXCEL SPREADSHEET [08-07-2025] FOR (1) IMPORTING; (2) COMPUTING SUMMARY STATISTICS FOR BP ESTIMATES; AND (3) SAVE AS STATA DATABASE ****
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. 
. 
. 
. 
. ** BSEM MODEL 1 (M1) [08-07-2024] ** 
. 
. 
. * IMPORT EXCEL DATABASE INTO STATA [NOTE: THIS IS LOCATED IN THE AP_Mplus FOLDER --> BSEM (AUGUST 2025) SUBFOLDER *
. 
. import excel "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M1.08-07-2025.xlsx", sheet(" bpsumstats.M1.08-07-2025") firstrow clear
{res}{text}(24 vars, 2,479 obs)

{com}. 
. 
. 
. * COMPUTE SUMMARY STATISTICS FOR BAYESIAN POSTERIOR (BP) ESTIMATES FROM MODEL 1 (M1) *
. 
. xtset okcoderev year, yearly
{res}
{col 1}{txt:Panel variable: }{res:okcoderev}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:year}{txt:, }{res:{bind:2002}}{txt: to }{res:{bind:2024}}{txt:, but with gaps}{p_end}
{txt}{col 10}Delta: {res}1 year
{txt}
{com}. *
. sum f1_mean_m1 f1_median_m1 f1_sd_m1 f1_25_m1 f1_975_m1    

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}f1_mean_m1 {c |}{res}      2,479    .0002557    .1999512      -.867       .612
{txt}f1_median_m1 {c |}{res}      2,479    .0002804    .2000185      -.866       .616
{txt}{space 4}f1_sd_m1 {c |}{res}      2,479    .0861327    .0456393       .041       .238
{txt}{space 4}f1_25_m1 {c |}{res}      2,479   -.1694494    .2197746      -1.04       .459
{txt}{space 3}f1_975_m1 {c |}{res}      2,479    .1683554    .2181054      -.689       .752
{txt}
{com}. *
. 
. 
. 
. * SAVE MODEL 1 (M1) BPSUMSTATS AS A STATA DATABASE [NOTE: MPLUS DATA --> STATA DATA UNDER "BSEM (AUGUST 2025)" SUBFOLDER] *
. 
. save "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M1.08-08-2025.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M1.08-08-2025.dta{rm}
saved
{p_end}

{com}. 
. 
. 
. ************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. ** BSEM MODEL 2 (M2) [08-07-2024] ** 
. 
. 
. * IMPORT EXCEL DATABASE INTO STATA [NOTE: THIS IS LOCATED IN THE AP_Mplus FOLDER --> BSEM (AUGUST 2025) SUBFOLDER *
. 
. import excel "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M2.08-07-2025.xlsx", sheet(" bpsumstats.M2.08-07-2025") firstrow clear
{res}{text}(36 vars, 2,498 obs)

{com}. 
. 
. 
. * COMPUTE SUMMARY STATISTICS FOR BAYESIAN POSTERIOR (BP) ESTIMATES FROM MODEL 2 (M2) *
. 
. xtset okcoderev year, yearly
{res}
{col 1}{txt:Panel variable: }{res:okcoderev}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:year}{txt:, }{res:{bind:2002}}{txt: to }{res:{bind:2024}}{txt:, but with gaps}{p_end}
{txt}{col 10}Delta: {res}1 year
{txt}
{com}. *
. sum f2_mean_m2 f2_median_m2 f2_sd_m2 f2_25_m2 f2_975_m2   f2_mean_m2 f2_median_m2 f2_sd_m2 f2_25_m2 f2_975_m2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}f2_mean_m2 {c |}{res}      2,498   -.0001753    .0335827      -.108      1.668
{txt}f2_median_m2 {c |}{res}      2,498   -.0001785    .0336711      -.111      1.672
{txt}{space 4}f2_sd_m2 {c |}{res}      2,498    .0044319     .009356       .001       .062
{txt}{space 4}f2_25_m2 {c |}{res}      2,498   -.0087902    .0359427      -.161      1.541
{txt}{space 3}f2_975_m2 {c |}{res}      2,498    .0086077    .0401331      -.042      1.779
{txt}{hline 13}{c +}{hline 57}
{space 2}f2_mean_m2 {c |}{res}      2,498   -.0001753    .0335827      -.108      1.668
{txt}f2_median_m2 {c |}{res}      2,498   -.0001785    .0336711      -.111      1.672
{txt}{space 4}f2_sd_m2 {c |}{res}      2,498    .0044319     .009356       .001       .062
{txt}{space 4}f2_25_m2 {c |}{res}      2,498   -.0087902    .0359427      -.161      1.541
{txt}{space 3}f2_975_m2 {c |}{res}      2,498    .0086077    .0401331      -.042      1.779
{txt}
{com}. *
. 
. 
. 
. * SAVE MODEL 2 (M2) BPSUMSTATS AS A STATA DATABASE [NOTE: MPLUS DATA --> STATA DATA UNDER "BSEM (AUGUST 2025)" SUBFOLDER] *
. 
. save "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M2.08-08-2025.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M2.08-08-2025.dta{rm}
saved
{p_end}

{com}. 
. 
. 
. *****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. ** BSEM MODEL 3 (M3) [08-07-2024] ** 
. 
. 
. * IMPORT EXCEL DATABASE INTO STATA [NOTE: THIS IS LOCATED IN THE AP_Mplus FOLDER --> BSEM (AUGUST 2025) SUBFOLDER *
. 
. import excel "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M3.08-07-2025.xlsx", sheet(" bpsumstats.M3.08-07-2025") firstrow clear
{res}{text}(20 vars, 2,476 obs)

{com}. 
. 
. 
. * COMPUTE SUMMARY STATISTICS FOR BAYESIAN POSTERIOR (BP) ESTIMATES FROM MODEL 3 (M3) *
. 
. xtset okcoderev year, yearly
{res}
{col 1}{txt:Panel variable: }{res:okcoderev}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:year}{txt:, }{res:{bind:2002}}{txt: to }{res:{bind:2024}}{txt:, but with gaps}{p_end}
{txt}{col 10}Delta: {res}1 year
{txt}
{com}. *
. sum f1_mean_m3 f1_median_m3 f1_sd_m3 f1_25_m3 f1_975_m3    

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}f1_mean_m3 {c |}{res}      2,476   -.0005319    .2017148      -.883        .61
{txt}f1_median_m3 {c |}{res}      2,476   -.0005218    .2018101      -.882        .61
{txt}{space 4}f1_sd_m3 {c |}{res}      2,476    .0868441    .0465767       .039       .245
{txt}{space 4}f1_25_m3 {c |}{res}      2,476   -.1718655    .2216486      -1.06       .478
{txt}{space 3}f1_975_m3 {c |}{res}      2,476    .1689705    .2213912      -.708       .753
{txt}
{com}. *
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. 
. 
. * SAVE MODEL 3 (M3) BPSUMSTATS AS A STATA DATABASE [NOTE: MPLUS DATA --> STATA DATA UNDER "BSEM (AUGUST 2025)" SUBFOLDER] *
. 
. save "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M3.08-08-2025.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M3.08-08-2025.dta{rm}
saved
{p_end}

{com}. 
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. 
. *****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
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. 
. 
. 
. ** BSEM MODEL 4 (M4) [08-07-2024] ** 
. 
. 
. * IMPORT EXCEL DATABASE INTO STATA [NOTE: THIS IS LOCATED IN THE AP_Mplus FOLDER --> BSEM (AUGUST 2025) SUBFOLDER *
. 
. import excel "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M4.08-07-2025.xlsx", sheet(" bpsumstats.M4.08-07-2025") firstrow clear
{res}{text}(32 vars, 2,495 obs)

{com}. 
. 
. 
. * COMPUTE SUMMARY STATISTICS FOR BAYESIAN POSTERIOR (BP) ESTIMATES FROM MODEL 4 (M4) *
. 
. xtset okcoderev year, yearly
{res}
{col 1}{txt:Panel variable: }{res:okcoderev}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:year}{txt:, }{res:{bind:2002}}{txt: to }{res:{bind:2024}}{txt:, but with gaps}{p_end}
{txt}{col 10}Delta: {res}1 year
{txt}
{com}. *
. sum f1_mean_m4 f1_median_m4 f1_sd_m4 f1_25_m4 f1_975_m4       f2_mean_m4 f2_median_m4 f2_sd_m4 f2_25_m4 f2_975_m4   

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}f1_mean_m4 {c |}{res}      2,495    .0000729    .2010918      -.882       .616
{txt}f1_median_m4 {c |}{res}      2,495    .0001427    .2010536      -.883        .62
{txt}{space 4}f1_sd_m4 {c |}{res}      2,495    .0879218    .0480179        .04       .248
{txt}{space 4}f1_25_m4 {c |}{res}      2,495   -.1732786    .2224348     -1.058       .472
{txt}{space 3}f1_975_m4 {c |}{res}      2,495    .1714228     .221816        -.7       .761
{txt}{hline 13}{c +}{hline 57}
{space 2}f2_mean_m4 {c |}{res}      2,495    .0000192    .0373446      -.034      1.857
{txt}f2_median_m4 {c |}{res}      2,495    .0001619     .038284      -.033      1.904
{txt}{space 4}f2_sd_m4 {c |}{res}      2,495    .0047699    .0106704       .001       .139
{txt}{space 4}f2_25_m4 {c |}{res}      2,495   -.0095283    .0373811      -.183      1.555
{txt}{space 3}f2_975_m4 {c |}{res}      2,495    .0095014    .0455709          0      2.039
{txt}
{com}. 
. 
. 
. * SAVE MODEL 4 (M4) BPSUMSTATS AS A STATA DATABASE [NOTE: MPLUS DATA --> STATA DATA UNDER "BSEM (AUGUST 2025)" SUBFOLDER] *
. 
. save "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M4.08-08-2025.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M4.08-08-2025.dta{rm}
saved
{p_end}

{com}. 
. 
. 
. *****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. ** BSEM MODEL 5 (M5) [08-07-2024] ** 
. 
. 
. * IMPORT EXCEL DATABASE INTO STATA [NOTE: THIS IS LOCATED IN THE AP_Mplus FOLDER --> BSEM (AUGUST 2025) SUBFOLDER *
. 
. import excel "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M5.08-07-2025.xlsx", sheet(" bpsumstats.M5.08-07-2025") firstrow clear
{res}{text}(29 vars, 2,479 obs)

{com}. 
. 
. 
. * COMPUTE SUMMARY STATISTICS FOR BAYESIAN POSTERIOR (BP) ESTIMATES FROM MODEL 5 (M5) *
. 
. xtset okcoderev year, yearly
{res}
{col 1}{txt:Panel variable: }{res:okcoderev}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:year}{txt:, }{res:{bind:2002}}{txt: to }{res:{bind:2024}}{txt:, but with gaps}{p_end}
{txt}{col 10}Delta: {res}1 year
{txt}
{com}. *
. sum f1_mean_m5 f1_median_m5 f1_sd_m5 f1_25_m5 f1_975_m5       f2_mean_m5 f2_median_m5 f2_sd_m5 f2_25_m5 f2_975_m5   

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}f1_mean_m5 {c |}{res}      2,479    .0000472    .2023106      -.881       .612
{txt}f1_median_m5 {c |}{res}      2,479    .0000609    .2023316      -.882       .612
{txt}{space 4}f1_sd_m5 {c |}{res}      2,479    .0851815    .0457731       .038       .237
{txt}{space 4}f1_25_m5 {c |}{res}      2,479   -.1679068    .2230985     -1.059       .464
{txt}{space 3}f1_975_m5 {c |}{res}      2,479    .1662315    .2197288      -.705        .76
{txt}{hline 13}{c +}{hline 57}
{space 2}f2_mean_m5 {c |}{res}      2,479   -.0000948    .2319679     -1.271      1.058
{txt}f2_median_m5 {c |}{res}      2,479   -.0000875    .2316669     -1.266      1.058
{txt}{space 4}f2_sd_m5 {c |}{res}      2,479    .2697027    .0812414       .139       .391
{txt}{space 4}f2_25_m5 {c |}{res}      2,479   -.5314421    .2938399     -1.689       .743
{txt}{space 3}f2_975_m5 {c |}{res}      2,479    .5269411     .272183      -.899      1.372
{txt}
{com}. *
. 
. 
. 
. * SAVE MODEL 5 (M5) BPSUMSTATS AS A STATA DATABASE [NOTE: MPLUS DATA --> STATA DATA UNDER "BSEM (AUGUST 2025)" SUBFOLDER] *
. 
. save "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M5.08-08-2025.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M5.08-08-2025.dta{rm}
saved
{p_end}

{com}. 
. 
. 
. *****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. ** BSEM MODEL 6 (M6) [08-07-2024] ** 
. 
. 
. * IMPORT EXCEL DATABASE INTO STATA [NOTE: THIS IS LOCATED IN THE AP_Mplus FOLDER --> BSEM (AUGUST 2025) SUBFOLDER *
. 
. import excel "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M6.08-07-2025.xlsx", sheet(" bpsumstats.M6.08-07-2025") firstrow clear
{res}{text}(29 vars, 2,479 obs)

{com}. 
. 
. 
. * COMPUTE SUMMARY STATISTICS FOR BAYESIAN POSTERIOR (BP) ESTIMATES FROM MODEL 6 (M6) *
. 
. xtset okcoderev year, yearly
{res}
{col 1}{txt:Panel variable: }{res:okcoderev}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:year}{txt:, }{res:{bind:2002}}{txt: to }{res:{bind:2024}}{txt:, but with gaps}{p_end}
{txt}{col 10}Delta: {res}1 year
{txt}
{com}. *
. sum f1_mean_m6 f1_median_m6 f1_sd_m6 f1_25_m6 f1_975_m6       f2_mean_m6 f2_median_m6 f2_sd_m6 f2_25_m6 f2_975_m6   

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}f1_mean_m6 {c |}{res}      2,479    .0000605    .2024364      -.881       .615
{txt}f1_median_m6 {c |}{res}      2,479     .000069    .2024429      -.882       .615
{txt}{space 4}f1_sd_m6 {c |}{res}      2,479    .0863021    .0461324       .038       .238
{txt}{space 4}f1_25_m6 {c |}{res}      2,479   -.1700682    .2229752     -1.063       .464
{txt}{space 3}f1_975_m6 {c |}{res}      2,479    .1684119    .2206137      -.706       .766
{txt}{hline 13}{c +}{hline 57}
{space 2}f2_mean_m6 {c |}{res}      2,479     -.00009    .2345448     -1.272      1.051
{txt}f2_median_m6 {c |}{res}      2,479   -.0001791    .2341498     -1.262      1.048
{txt}{space 4}f2_sd_m6 {c |}{res}      2,479      .27057    .0813413        .14       .393
{txt}{space 4}f2_25_m6 {c |}{res}      2,479    -.532881    .2957678     -1.689       .734
{txt}{space 3}f2_975_m6 {c |}{res}      2,479    .5287838    .2742281      -.891      1.373
{txt}
{com}. *
. 
. 
. 
. * SAVE MODEL 6 (M6) BPSUMSTATS AS A STATA DATABASE [NOTE: MPLUS DATA --> STATA DATA UNDER "BSEM (AUGUST 2025)" SUBFOLDER] *
. 
. save "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M6.08-08-2025.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M6.08-08-2025.dta{rm}
saved
{p_end}

{com}. 
. 
. 
. *****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. ** BSEM MODEL 7 (M7) [08-07-2024] ** 
. 
. 
. * IMPORT EXCEL DATABASE INTO STATA [NOTE: THIS IS LOCATED IN THE AP_Mplus FOLDER --> BSEM (AUGUST 2025) SUBFOLDER *
. 
. import excel "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M7.08-07-2025.xlsx", sheet(" bpsumstats.M7.08-07-2025") firstrow clear
{res}{text}(22 vars, 2,479 obs)

{com}. 
. 
. 
. * COMPUTE SUMMARY STATISTICS FOR BAYESIAN POSTERIOR (BP) ESTIMATES FROM MODEL 7 (M7) *
. 
. xtset okcoderev year, yearly
{res}
{col 1}{txt:Panel variable: }{res:okcoderev}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:year}{txt:, }{res:{bind:2002}}{txt: to }{res:{bind:2024}}{txt:, but with gaps}{p_end}
{txt}{col 10}Delta: {res}1 year
{txt}
{com}. *
. sum f1_mean_m7 f1_median_m7 f1_sd_m7 f1_25_m7 f1_975_m7

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}f1_mean_m7 {c |}{res}      2,479   -.0004417     .200339       -.87       .615
{txt}f1_median_m7 {c |}{res}      2,479   -.0004905    .2002412      -.868       .616
{txt}{space 4}f1_sd_m7 {c |}{res}      2,479    .0860145    .0457845       .037       .284
{txt}{space 4}f1_25_m7 {c |}{res}      2,479     -.16967    .2202247     -1.047        .47
{txt}{space 3}f1_975_m7 {c |}{res}      2,479    .1673622    .2191605        -.7       .767
{txt}
{com}. *
. 
. 
. 
. * SAVE MODEL 7 (M7) BPSUMSTATS AS A STATA DATABASE [NOTE: MPLUS DATA --> STATA DATA UNDER "BSEM (AUGUST 2025)" SUBFOLDER] *
. 
. save "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M7.08-08-2025.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M7.08-08-2025.dta{rm}
saved
{p_end}

{com}. 
. 
. 
. *****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. ** BSEM MODEL 8 (M8) [08-07-2024] ** 
. 
. 
. 
. 
. * IMPORT EXCEL DATABASE INTO STATA [NOTE: THIS IS LOCATED IN THE AP_Mplus FOLDER --> BSEM (AUGUST 2025) SUBFOLDER *
. 
. import excel "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M8.08-07-2025.xlsx", sheet(" bpsumstats.M8.08-07-2025") firstrow clear
{res}{text}(25 vars, 2,479 obs)

{com}. 
. 
. 
. * COMPUTE SUMMARY STATISTICS FOR BAYESIAN POSTERIOR (BP) ESTIMATES FROM MODEL 8 (M8) *
. 
. xtset okcoderev year, yearly
{res}
{col 1}{txt:Panel variable: }{res:okcoderev}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:year}{txt:, }{res:{bind:2002}}{txt: to }{res:{bind:2024}}{txt:, but with gaps}{p_end}
{txt}{col 10}Delta: {res}1 year
{txt}
{com}. *
. sum f1_mean_m8 f1_median_m8 f1_sd_m8 f1_25_m8 f1_975_m8

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}f1_mean_m8 {c |}{res}      2,479    6.45e-06    .2000709       -.87       .611
{txt}f1_median_m8 {c |}{res}      2,479    6.86e-06    .2000702      -.871       .613
{txt}{space 4}f1_sd_m8 {c |}{res}      2,479    .0863667    .0458427       .038       .235
{txt}{space 4}f1_25_m8 {c |}{res}      2,479   -.1702509    .2201796     -1.049       .455
{txt}{space 3}f1_975_m8 {c |}{res}      2,479     .168476    .2186256      -.702       .759
{txt}
{com}. 
. 
. 
. * SAVE MODEL 8 (M8) BPSUMSTATS AS A STATA DATABASE [NOTE: MPLUS DATA --> STATA DATA UNDER "BSEM (AUGUST 2025)" SUBFOLDER] *
. 
. save "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M8.08-08-2025.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M8.08-08-2025.dta{rm}
saved
{p_end}

{com}. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. 
. **** STEP 2: MERGE DATABASES IN A CUMULATIVE MANNER ONE DATABASE AT A TIME IN A SEQUENTIAL MANNER  ****
. 
. 
. 
. 
. 
. 
. 
. 
. ** ACTIVATE MODEL 1 (M1) IN MEMORY AND SUBSEQUENTLY MERGE MODEL 2 (M2) INTO MODEL 1 (M1) ** 
. 
. use "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M1.08-08-2025.dta", replace
{txt}
{com}. *
. merge 1:1 okcoderev okcodep year using "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M2.08-08-2025.dta"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}              19
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}              19{txt}  (_merge==2)

{col 5}Matched{col 30}{res}           2,479{txt}  (_merge==3)
{col 5}{hline 41}

{com}. *
. rename _merge _merge12
{res}{txt}
{com}. *
. save "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.COMBINED_M1_M2.08-08-2025.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.COMBINED_M1_M2.08-08-2025.dta{rm}
saved
{p_end}

{com}. 
. *
. *
. *
. 
. 
. ** RETRIEVE COMBINED MODELS 1 (M1) & 2 (M2) AND MERGE THESE BP MODEL ESTIMATES WITH THOSE FROM MODEL 3 (M3) ** 
. 
. use "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.COMBINED_M1_M2.08-08-2025.dta", replace
{txt}
{com}. 
. 
. merge 1:1 okcoderev okcodep year using "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M3.08-08-2025.dta"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}              22
{txt}{col 9}from master{col 30}{res}              22{txt}  (_merge==1)
{col 9}from using{col 30}{res}               0{txt}  (_merge==2)

{col 5}Matched{col 30}{res}           2,476{txt}  (_merge==3)
{col 5}{hline 41}

{com}. *
. rename _merge _merge123
{res}{txt}
{com}. *
. save "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.COMBINED_M1_M3.08-08-2025.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.COMBINED_M1_M3.08-08-2025.dta{rm}
saved
{p_end}

{com}. 
. *
. *
. *
. 
. 
. ** RETRIEVE COMBINED MODELS 1 (M1), 2 (M2), & 3 (M3) AND MERGE THESE BP MODEL ESTIMATES WITH THOSE FROM MODEL 4 (M4) ** 
. 
. use "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.COMBINED_M1_M3.08-08-2025.dta", replace
{txt}
{com}. 
. 
. merge 1:1 okcoderev okcodep year using "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M4.08-08-2025.dta"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}               3
{txt}{col 9}from master{col 30}{res}               3{txt}  (_merge==1)
{col 9}from using{col 30}{res}               0{txt}  (_merge==2)

{col 5}Matched{col 30}{res}           2,495{txt}  (_merge==3)
{col 5}{hline 41}

{com}. *
. rename _merge _merge1234
{res}{txt}
{com}. *
. save "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.COMBINED_M1_M4.08-08-2025.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.COMBINED_M1_M4.08-08-2025.dta{rm}
saved
{p_end}

{com}. 
. *
. *
. *
. 
. 
. 
. ** RETRIEVE COMBINED MODELS 1 (M1), 2 (M2), 3 (M3), & 4 (M4) AND MERGE THESE BP MODEL ESTIMATES WITH THOSE FROM MODEL 5 (M5) ** 
. 
. use "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.COMBINED_M1_M4.08-08-2025.dta", replace
{txt}
{com}. 
. 
. merge 1:1 okcoderev okcodep year using "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M5.08-08-2025.dta"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}              19
{txt}{col 9}from master{col 30}{res}              19{txt}  (_merge==1)
{col 9}from using{col 30}{res}               0{txt}  (_merge==2)

{col 5}Matched{col 30}{res}           2,479{txt}  (_merge==3)
{col 5}{hline 41}

{com}. *
. rename _merge _merge12345
{res}{txt}
{com}. *
. save "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.COMBINED_M1_M5.08-08-2025.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.COMBINED_M1_M5.08-08-2025.dta{rm}
saved
{p_end}

{com}. 
. *
. *
. *
. 
. 
. 
. 
. ** RETRIEVE COMBINED MODELS 1 (M1), 2 (M2), 3 (M3), 4 (M4), & 5 (M5) AND MERGE THESE BP MODEL ESTIMATES WITH THOSE FROM MODEL 6 (M6) ** 
. 
. use "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.COMBINED_M1_M5.08-08-2025.dta", replace
{txt}
{com}. 
. 
. merge 1:1 okcoderev okcodep year using "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M6.08-08-2025.dta"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}              19
{txt}{col 9}from master{col 30}{res}              19{txt}  (_merge==1)
{col 9}from using{col 30}{res}               0{txt}  (_merge==2)

{col 5}Matched{col 30}{res}           2,479{txt}  (_merge==3)
{col 5}{hline 41}

{com}. *
. rename _merge _merge123456
{res}{txt}
{com}. *
. save "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.COMBINED_M1_M6.08-08-2025.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.COMBINED_M1_M6.08-08-2025.dta{rm}
saved
{p_end}

{com}. 
. *
. *
. *
. 
. 
. 
. ** RETRIEVE COMBINED MODELS 1 (M1), 2 (M2), 3 (M3), 4 (M4), 5 (M5), & 6 (M6) AND MERGE THESE BP MODEL ESTIMATES WITH THOSE FROM MODEL 7 (M7) ** 
. 
. use "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.COMBINED_M1_M6.08-08-2025.dta", replace
{txt}
{com}. 
. 
. merge 1:1 okcoderev okcodep year using "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M7.08-08-2025.dta"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}              19
{txt}{col 9}from master{col 30}{res}              19{txt}  (_merge==1)
{col 9}from using{col 30}{res}               0{txt}  (_merge==2)

{col 5}Matched{col 30}{res}           2,479{txt}  (_merge==3)
{col 5}{hline 41}

{com}. *
. rename _merge _merge1234567
{res}{txt}
{com}. *
. save "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.COMBINED_M1_M7.08-08-2025.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.COMBINED_M1_M7.08-08-2025.dta{rm}
saved
{p_end}

{com}. 
. *
. *
. *
. 
. 
. 
. 
. 
. ** RETRIEVE COMBINED MODELS 1 (M1), 2 (M2), 3 (M3), 4 (M4), 5 (M5), 6 (M6), & 7 (M7) AND MERGE THESE BP MODEL ESTIMATES WITH THOSE FROM MODEL 8 (M8) ** 
. 
. use "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.COMBINED_M1_M7.08-08-2025.dta", replace
{txt}
{com}. 
. 
. merge 1:1 okcoderev okcodep year using "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.M8.08-08-2025.dta"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}              19
{txt}{col 9}from master{col 30}{res}              19{txt}  (_merge==1)
{col 9}from using{col 30}{res}               0{txt}  (_merge==2)

{col 5}Matched{col 30}{res}           2,479{txt}  (_merge==3)
{col 5}{hline 41}

{com}. *
. rename _merge _merge12345678
{res}{txt}
{com}. *
. save "C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.COMBINED_M1_M8.08-08-2025.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats.COMBINED_M1_M8.08-08-2025.dta{rm}
saved
{p_end}

{com}. 
. *
. *
. *
. 
. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. 
. **** STEP 3: COMPUTE DESCRIPTIVE STATISTICS FROM THE OMNIBUS DATABASE [MODEL 1 - MODEL 8] OF POSTERIOR FACTOR SCORE ESTIMATES [POOLED & PANEL STATISTICS]  ****
. 
. 
. 
. ****         NOTE: DESCRIPTIVE STATISTICS ARE COMPUTED ON COMMON SAMPLE OF N = 2,476 OBSERVATIONS [MODEL 3] -- WITH MODELS 1 & MODELS 5-8: N = 2,479 OBSERVATIONS   ***** 
. 
. 
. 
. 
. 
. xtset okcoderev year, yearly
{res}
{col 1}{txt:Panel variable: }{res:okcoderev}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:year}{txt:, }{res:{bind:2002}}{txt: to }{res:{bind:2024}}{txt:, but with gaps}{p_end}
{txt}{col 10}Delta: {res}1 year
{txt}
{com}. *
. *
. *
. sum    f1_mean_m1 f1_median_m1 f1_sd_m1 f1_25_m1  f1_975_m1 if f1_median_m3!=., detail   

                         {txt}f1_mean_m1
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.558          -.867
{txt} 5%    {res}    -.352          -.819
{txt}10%    {res}     -.24           -.79       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.112          -.787       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    -.001                      {txt}Mean          {res} .0004883
                        {txt}Largest       Std. dev.     {res} .1999388
{txt}75%    {res}      .12           .553
{txt}90%    {res}     .253           .576       {txt}Variance      {res} .0399755
{txt}95%    {res}     .335           .587       {txt}Skewness      {res}-.2716601
{txt}99%    {res}     .466           .612       {txt}Kurtosis      {res} 3.843165

                        {txt}f1_median_m1
{hline 61}
      Percentiles      Smallest
 1%    {res}     -.56          -.866
{txt} 5%    {res}    -.353          -.818
{txt}10%    {res}    -.239          -.787       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.112          -.786       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    -.001                      {txt}Mean          {res} .0005105
                        {txt}Largest       Std. dev.     {res} .2000087
{txt}75%    {res}      .12           .556
{txt}90%    {res}     .252           .576       {txt}Variance      {res} .0400035
{txt}95%    {res}     .335           .589       {txt}Skewness      {res}-.2708963
{txt}99%    {res}     .466           .616       {txt}Kurtosis      {res} 3.839842

                          {txt}f1_sd_m1
{hline 61}
      Percentiles      Smallest
 1%    {res}     .042           .041
{txt} 5%    {res}     .044           .041
{txt}10%    {res}     .048           .041       {txt}Obs         {res}      2,476
{txt}25%    {res}     .064           .041       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     .071                      {txt}Mean          {res} .0860727
                        {txt}Largest       Std. dev.     {res} .0456342
{txt}75%    {res}     .088           .234
{txt}90%    {res}     .133           .236       {txt}Variance      {res} .0020825
{txt}95%    {res}     .219           .236       {txt}Skewness      {res} 2.200432
{txt}99%    {res}     .228           .238       {txt}Kurtosis      {res} 6.913249

                          {txt}f1_25_m1
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.721          -1.04
{txt} 5%    {res}    -.513          -.999
{txt}10%    {res}    -.452          -.962       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.321          -.928       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     -.16                      {txt}Mean          {res}-.1691103
                        {txt}Largest       Std. dev.     {res}   .21967
{txt}75%    {res}    -.023           .431
{txt}90%    {res}     .108           .431       {txt}Variance      {res} .0482549
{txt}95%    {res}      .18           .446       {txt}Skewness      {res}-.1705956
{txt}99%    {res}     .312           .459       {txt}Kurtosis      {res} 3.037963

                          {txt}f1_975_m1
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.386          -.689
{txt} 5%    {res}    -.215          -.657
{txt}10%    {res}      -.1          -.637       {txt}Obs         {res}      2,476
{txt}25%    {res}    .0285          -.604       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     .164                      {txt}Mean          {res} .1684669
                        {txt}Largest       Std. dev.     {res} .2181961
{txt}75%    {res}     .332           .721
{txt}90%    {res}     .442           .721       {txt}Variance      {res} .0476096
{txt}95%    {res}     .493           .741       {txt}Skewness      {res}-.2721164
{txt}99%    {res}      .63           .752       {txt}Kurtosis      {res} 3.090908
{txt}
{com}. *
. xtsum  f1_mean_m1 f1_median_m1 f1_sd_m1 f1_25_m1  f1_975_m1 if f1_median_m3!=.

{txt}Variable         {c |}      Mean   Std. dev.       Min        Max {c |}    Observations
{hline 17}{c +}{hline 44}{c +}{hline 16}
f1_mea~1{col 10}overall {c |} {res} .0004883   .1999388      -.867       .612{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res}  .142535      -.455   .2932632{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1449485  -.7714064    .684962{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_med~1{col 10}overall {c |} {res} .0005105   .2000087      -.866       .616{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1426243      -.456   .2924211{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1449763  -.7710684   .6868789{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_sd_m1{col 10}overall {c |} {res} .0860727   .0456342       .041       .238{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .0330193       .048   .2228333{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .0323416  -.0111378    .240906{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_25_m1{col 10}overall {c |} {res}-.1691103     .21967      -1.04       .459{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1538028      -.546   .1662632{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1601546  -.8537418    .549995{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_975~1{col 10}overall {c |} {res} .1684669   .2181961      -.689       .752{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1587646      -.356   .4765263{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1562516  -.6827963   .8214669{txt} {c |} T-bar = 18.3407

{com}. *
. *
. *
. sum    f1_mean_m2 f1_median_m2 f1_sd_m2 f1_25_m2  f1_975_m2      f2_mean_m2 f2_median_m2 f2_sd_m2 f2_25_m2 f2_975_m2 if f1_median_m3!=., detail

                         {txt}f1_mean_m2
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.551          -.875
{txt} 5%    {res}    -.353          -.817
{txt}10%    {res}    -.241          -.792       {txt}Obs         {res}      2,476
{txt}25%    {res}   -.1115          -.789       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}   -.0015                      {txt}Mean          {res} .0004305
                        {txt}Largest       Std. dev.     {res} .2002138
{txt}75%    {res}     .121           .553
{txt}90%    {res}     .252           .584       {txt}Variance      {res} .0400856
{txt}95%    {res}     .335           .588       {txt}Skewness      {res}-.2715327
{txt}99%    {res}     .466           .611       {txt}Kurtosis      {res} 3.845532

                        {txt}f1_median_m2
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.551          -.877
{txt} 5%    {res}    -.352          -.817
{txt}10%    {res}    -.241          -.792       {txt}Obs         {res}      2,476
{txt}25%    {res}   -.1115          -.788       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    -.001                      {txt}Mean          {res} .0004321
                        {txt}Largest       Std. dev.     {res} .2002257
{txt}75%    {res}    .1205           .552
{txt}90%    {res}     .252           .582       {txt}Variance      {res} .0400903
{txt}95%    {res}     .334           .586       {txt}Skewness      {res}-.2733224
{txt}99%    {res}     .464           .609       {txt}Kurtosis      {res} 3.847336

                          {txt}f1_sd_m2
{hline 61}
      Percentiles      Smallest
 1%    {res}     .041           .039
{txt} 5%    {res}     .044            .04
{txt}10%    {res}     .048            .04       {txt}Obs         {res}      2,476
{txt}25%    {res}    .0645            .04       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     .071                      {txt}Mean          {res} .0863417
                        {txt}Largest       Std. dev.     {res} .0460952
{txt}75%    {res}    .0885           .234
{txt}90%    {res}     .134           .236       {txt}Variance      {res} .0021248
{txt}95%    {res}     .221           .236       {txt}Skewness      {res} 2.199388
{txt}99%    {res}      .23           .237       {txt}Kurtosis      {res} 6.902328

                          {txt}f1_25_m2
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.726         -1.055
{txt} 5%    {res}    -.517         -1.012
{txt}10%    {res}    -.458          -.971       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.321           -.92       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    -.158                      {txt}Mean          {res}-.1697242
                        {txt}Largest       Std. dev.     {res} .2207562
{txt}75%    {res}   -.0225           .429
{txt}90%    {res}      .11            .43       {txt}Variance      {res} .0487333
{txt}95%    {res}     .181           .446       {txt}Skewness      {res}-.1732266
{txt}99%    {res}     .311           .463       {txt}Kurtosis      {res} 3.025239

                          {txt}f1_975_m2
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.385          -.696
{txt} 5%    {res}    -.209          -.661
{txt}10%    {res}    -.098          -.628       {txt}Obs         {res}      2,476
{txt}25%    {res}     .026          -.606       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     .165                      {txt}Mean          {res} .1688679
                        {txt}Largest       Std. dev.     {res} .2189075
{txt}75%    {res}    .3355           .727
{txt}90%    {res}     .443            .74       {txt}Variance      {res} .0479205
{txt}95%    {res}     .498           .755       {txt}Skewness      {res}-.2682516
{txt}99%    {res}     .626           .758       {txt}Kurtosis      {res} 3.073995

                         {txt}f2_mean_m2
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.007          -.108
{txt} 5%    {res}    -.002          -.025
{txt}10%    {res}    -.002          -.024       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.002          -.024       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    -.001                      {txt}Mean          {res} -.000187
                        {txt}Largest       Std. dev.     {res} .0337281
{txt}75%    {res}        0            .03
{txt}90%    {res}     .001            .03       {txt}Variance      {res} .0011376
{txt}95%    {res}     .003           .032       {txt}Skewness      {res} 48.88308
{txt}99%    {res}     .009          1.668       {txt}Kurtosis      {res} 2418.895

                        {txt}f2_median_m2
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.009          -.111
{txt} 5%    {res}    -.002          -.026
{txt}10%    {res}    -.002          -.025       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.002          -.023       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    -.001                      {txt}Mean          {res}-.0001878
                        {txt}Largest       Std. dev.     {res} .0338176
{txt}75%    {res}        0            .03
{txt}90%    {res}     .001           .031       {txt}Variance      {res} .0011436
{txt}95%    {res}     .003           .031       {txt}Skewness      {res} 48.84464
{txt}99%    {res}     .009          1.672       {txt}Kurtosis      {res} 2416.439

                          {txt}f2_sd_m2
{hline 61}
      Percentiles      Smallest
 1%    {res}     .001           .001
{txt} 5%    {res}     .001           .001
{txt}10%    {res}     .001           .001       {txt}Obs         {res}      2,476
{txt}25%    {res}     .001           .001       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     .001                      {txt}Mean          {res} .0043643
                        {txt}Largest       Std. dev.     {res} .0092651
{txt}75%    {res}     .002           .043
{txt}90%    {res}     .003           .045       {txt}Variance      {res} .0000858
{txt}95%    {res}     .033           .046       {txt}Skewness      {res} 2.935644
{txt}99%    {res}     .038           .062       {txt}Kurtosis      {res} 10.03746

                          {txt}f2_25_m2
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.077          -.161
{txt} 5%    {res}    -.063          -.121
{txt}10%    {res}    -.006          -.117       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.005          -.103       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    -.004                      {txt}Mean          {res}-.0086805
                        {txt}Largest       Std. dev.     {res} .0360137
{txt}75%    {res}    -.003           .006
{txt}90%    {res}    -.003           .007       {txt}Variance      {res}  .001297
{txt}95%    {res}    -.002           .007       {txt}Skewness      {res} 31.78602
{txt}99%    {res}        0          1.541       {txt}Kurtosis      {res} 1386.612

                          {txt}f2_975_m2
{hline 61}
      Percentiles      Smallest
 1%    {res}        0          -.042
{txt} 5%    {res}        0              0
{txt}10%    {res}     .001              0       {txt}Obs         {res}      2,476
{txt}25%    {res}     .001              0       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     .002                      {txt}Mean          {res} .0084665
                        {txt}Largest       Std. dev.     {res} .0401602
{txt}75%    {res}     .003           .099
{txt}90%    {res}     .008           .102       {txt}Variance      {res} .0016128
{txt}95%    {res}     .064           .106       {txt}Skewness      {res} 34.91474
{txt}99%    {res}      .08          1.779       {txt}Kurtosis      {res}  1527.45
{txt}
{com}. *
. xtsum  f1_mean_m2 f1_median_m2 f1_sd_m2 f1_25_m2  f1_975_m2      f2_mean_m2 f2_median_m2 f2_sd_m2 f2_25_m2 f2_975_m2 if f1_median_m3!=.

{txt}Variable         {c |}      Mean   Std. dev.       Min        Max {c |}    Observations
{hline 17}{c +}{hline 44}{c +}{hline 16}
f1_mea~2{col 10}overall {c |} {res} .0004305   .2002138      -.875       .611{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1426111      -.451   .2936842{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1451545   -.778201   .6787463{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_med~2{col 10}overall {c |} {res} .0004321   .2002257      -.877       .609{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1425974      -.452   .2941579{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res}  .145204  -.7802521   .6790637{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_sd_m2{col 10}overall {c |} {res} .0863417   .0460952       .039       .237{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .0334046       .047   .2251667{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .0326263  -.0085004   .2381195{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_25_m2{col 10}overall {c |} {res}-.1697242   .2207562     -1.055       .463{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1546237      -.546   .1662105{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res}  .160891  -.8689873    .557539{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_975~2{col 10}overall {c |} {res} .1688679   .2189075      -.696       .758{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1592613      -.357   .4888421{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1566956  -.6883952   .8068679{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f2_mea~2{col 10}overall {c |} {res} -.000187   .0337281      -.108      1.668{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .0076669      -.008   .0870526{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res}  .032821   -.100187    1.58076{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f2_med~2{col 10}overall {c |} {res}-.0001878   .0338176      -.111      1.672{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .0076839  -.0081667   .0871579{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .0329088  -.1030211   1.584654{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f2_sd_m2{col 10}overall {c |} {res} .0043643   .0092651       .001       .062{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .0022353   .0011667   .0122632{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .0089912  -.0058989   .0541011{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f2_25_m2{col 10}overall {c |} {res}-.0086805   .0360137      -.161      1.541{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .0075976  -.0205789   .0626842{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .0351918  -.1523647   1.469635{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f2_975~2{col 10}overall {c |} {res} .0084665   .0401602      -.042      1.779{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .0097433   .0011667   .1106316{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .0389315  -.1001651   1.676835{txt} {c |} T-bar = 18.3407

{com}. *
. *
. *
. sum    f1_mean_m3 f1_median_m3  f1_sd_m3  f1_25_m3  f1_975_m3 if f1_median_m3!=., detail

                         {txt}f1_mean_m3
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.569          -.883
{txt} 5%    {res}    -.359          -.827
{txt}10%    {res}    -.245          -.803       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.113          -.799       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}   -.0005                      {txt}Mean          {res}-.0005319
                        {txt}Largest       Std. dev.     {res} .2017148
{txt}75%    {res}     .123           .542
{txt}90%    {res}     .254            .56       {txt}Variance      {res} .0406889
{txt}95%    {res}     .329           .584       {txt}Skewness      {res}-.3534461
{txt}99%    {res}     .463            .61       {txt}Kurtosis      {res} 3.910619

                        {txt}f1_median_m3
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.568          -.882
{txt} 5%    {res}     -.36          -.827
{txt}10%    {res}    -.246          -.803       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.114          -.801       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}        0                      {txt}Mean          {res}-.0005218
                        {txt}Largest       Std. dev.     {res} .2018101
{txt}75%    {res}     .124           .541
{txt}90%    {res}     .253           .562       {txt}Variance      {res} .0407273
{txt}95%    {res}      .33           .585       {txt}Skewness      {res}-.3537682
{txt}99%    {res}     .463            .61       {txt}Kurtosis      {res} 3.908357

                          {txt}f1_sd_m3
{hline 61}
      Percentiles      Smallest
 1%    {res}     .041           .039
{txt} 5%    {res}     .048           .039
{txt}10%    {res}     .051           .039       {txt}Obs         {res}      2,476
{txt}25%    {res}     .064           .039       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     .071                      {txt}Mean          {res} .0868441
                        {txt}Largest       Std. dev.     {res} .0465767
{txt}75%    {res}     .088           .236
{txt}90%    {res}     .135           .238       {txt}Variance      {res} .0021694
{txt}95%    {res}     .222           .239       {txt}Skewness      {res} 2.228128
{txt}99%    {res}     .231           .245       {txt}Kurtosis      {res} 6.915141

                          {txt}f1_25_m3
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.732          -1.06
{txt} 5%    {res}    -.519         -1.023
{txt}10%    {res}     -.46          -.975       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.326          -.931       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    -.157                      {txt}Mean          {res}-.1718655
                        {txt}Largest       Std. dev.     {res} .2216486
{txt}75%    {res}    -.021            .41
{txt}90%    {res}     .106           .414       {txt}Variance      {res} .0491281
{txt}95%    {res}      .18           .424       {txt}Skewness      {res}-.2139529
{txt}99%    {res}     .306           .478       {txt}Kurtosis      {res} 3.038649

                          {txt}f1_975_m3
{hline 61}
      Percentiles      Smallest
 1%    {res}      -.4          -.708
{txt} 5%    {res}    -.222          -.669
{txt}10%    {res}    -.104          -.655       {txt}Obs         {res}      2,476
{txt}25%    {res}     .025          -.642       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     .165                      {txt}Mean          {res} .1689705
                        {txt}Largest       Std. dev.     {res} .2213912
{txt}75%    {res}      .34           .699
{txt}90%    {res}     .443           .731       {txt}Variance      {res} .0490141
{txt}95%    {res}     .491           .743       {txt}Skewness      {res}-.3478274
{txt}99%    {res}      .62           .753       {txt}Kurtosis      {res} 3.132505
{txt}
{com}. *
. xtsum  f1_mean_m3 f1_median_m3  f1_sd_m3  f1_25_m3  f1_975_m3 if f1_median_m3!=.

{txt}Variable         {c |}      Mean   Std. dev.       Min        Max {c |}    Observations
{hline 17}{c +}{hline 44}{c +}{hline 16}
f1_mea~3{col 10}overall {c |} {res}-.0005319   .2017148      -.883        .61{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res}  .144395      -.457       .292{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1455947  -.7834793   .6715734{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_med~3{col 10}overall {c |} {res}-.0005218   .2018101      -.882        .61{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1444475      -.459   .2913684{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1457167  -.7828376   .6712677{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_sd_m3{col 10}overall {c |} {res} .0868441   .0465767       .039       .245{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .0330667       .049   .2247778{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .0336228  -.0240506   .2403704{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_25_m3{col 10}overall {c |} {res}-.1718655   .2216486      -1.06       .478{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1554111      -.545   .1509333{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1613098  -.8714445   .5319766{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_975~3{col 10}overall {c |} {res} .1689705   .2213912      -.708       .753{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1609055      -.359   .4780526{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1585923   -.691819   .8090231{txt} {c |} T-bar = 18.3407

{com}. *
. *
. *
. sum    f1_mean_m4 f1_median_m4 f1_sd_m4 f1_25_m4  f1_975_m4      f2_mean_m4 f2_median_m4 f2_sd_m4 f2_25_m4 f2_975_m4 if f1_median_m3!=., detail

                         {txt}f1_mean_m4
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.566          -.882
{txt} 5%    {res}    -.359          -.828
{txt}10%    {res}    -.246          -.804       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.114          -.799       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}   -.0005                      {txt}Mean          {res} .0000594
                        {txt}Largest       Std. dev.     {res} .2018604
{txt}75%    {res}     .124           .536
{txt}90%    {res}     .255           .555       {txt}Variance      {res} .0407476
{txt}95%    {res}      .33           .586       {txt}Skewness      {res}-.3528407
{txt}99%    {res}     .459           .616       {txt}Kurtosis      {res}  3.91056

                        {txt}f1_median_m4
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.567          -.883
{txt} 5%    {res}    -.359          -.827
{txt}10%    {res}    -.246          -.807       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.113          -.797       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     .001                      {txt}Mean          {res} .0001527
                        {txt}Largest       Std. dev.     {res} .2018218
{txt}75%    {res}     .124           .537
{txt}90%    {res}     .255           .554       {txt}Variance      {res}  .040732
{txt}95%    {res}     .328           .585       {txt}Skewness      {res} -.353243
{txt}99%    {res}     .457            .62       {txt}Kurtosis      {res} 3.909403

                          {txt}f1_sd_m4
{hline 61}
      Percentiles      Smallest
 1%    {res}     .042            .04
{txt} 5%    {res}     .048            .04
{txt}10%    {res}     .052            .04       {txt}Obs         {res}      2,476
{txt}25%    {res}     .064           .041       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     .071                      {txt}Mean          {res} .0868768
                        {txt}Largest       Std. dev.     {res} .0466881
{txt}75%    {res}     .088           .236
{txt}90%    {res}     .136           .238       {txt}Variance      {res} .0021798
{txt}95%    {res}     .222           .243       {txt}Skewness      {res} 2.239529
{txt}99%    {res}      .23           .248       {txt}Kurtosis      {res} 6.934677

                          {txt}f1_25_m4
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.738         -1.058
{txt} 5%    {res}    -.518             -1
{txt}10%    {res}     -.46          -.966       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.325          -.943       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}   -.1595                      {txt}Mean          {res}-.1712407
                        {txt}Largest       Std. dev.     {res} .2220574
{txt}75%    {res}    -.022           .416
{txt}90%    {res}     .106           .419       {txt}Variance      {res} .0493095
{txt}95%    {res}     .182           .426       {txt}Skewness      {res}-.2118358
{txt}99%    {res}     .305           .472       {txt}Kurtosis      {res} 3.025437

                          {txt}f1_975_m4
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.398            -.7
{txt} 5%    {res}     -.22          -.675
{txt}10%    {res}    -.105          -.669       {txt}Obs         {res}      2,476
{txt}25%    {res}     .026          -.655       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     .166                      {txt}Mean          {res} .1693554
                        {txt}Largest       Std. dev.     {res} .2213943
{txt}75%    {res}    .3435           .704
{txt}90%    {res}     .444           .707       {txt}Variance      {res} .0490155
{txt}95%    {res}     .495            .76       {txt}Skewness      {res}-.3473861
{txt}99%    {res}     .625           .761       {txt}Kurtosis      {res} 3.132305

                         {txt}f2_mean_m4
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.012          -.034
{txt} 5%    {res}    -.002          -.031
{txt}10%    {res}    -.002          -.028       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.002          -.024       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    -.001                      {txt}Mean          {res} .0000315
                        {txt}Largest       Std. dev.     {res} .0374869
{txt}75%    {res}        0           .028
{txt}90%    {res}     .001            .03       {txt}Variance      {res} .0014053
{txt}95%    {res}     .003            .03       {txt}Skewness      {res} 49.12319
{txt}99%    {res}     .012          1.857       {txt}Kurtosis      {res} 2433.883

                        {txt}f2_median_m4
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.012          -.033
{txt} 5%    {res}    -.002          -.032
{txt}10%    {res}    -.002          -.028       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.001          -.022       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    -.001                      {txt}Mean          {res} .0001684
                        {txt}Largest       Std. dev.     {res} .0384301
{txt}75%    {res}        0           .028
{txt}90%    {res}     .001           .029       {txt}Variance      {res} .0014769
{txt}95%    {res}     .003           .037       {txt}Skewness      {res} 49.13434
{txt}99%    {res}     .012          1.904       {txt}Kurtosis      {res} 2434.612

                          {txt}f2_sd_m4
{hline 61}
      Percentiles      Smallest
 1%    {res}     .001           .001
{txt} 5%    {res}     .001           .001
{txt}10%    {res}     .001           .001       {txt}Obs         {res}      2,476
{txt}25%    {res}     .001           .001       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     .002                      {txt}Mean          {res} .0047403
                        {txt}Largest       Std. dev.     {res} .0106465
{txt}75%    {res}     .002           .048
{txt}90%    {res}     .003           .056       {txt}Variance      {res} .0001133
{txt}95%    {res}     .036           .109       {txt}Skewness      {res} 3.655173
{txt}99%    {res}     .042           .139       {txt}Kurtosis      {res} 21.79756

                          {txt}f2_25_m4
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.089          -.183
{txt} 5%    {res}     -.07          -.119
{txt}10%    {res}    -.006           -.11       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.006          -.109       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    -.004                      {txt}Mean          {res}-.0094584
                        {txt}Largest       Std. dev.     {res} .0374516
{txt}75%    {res}    -.003           .007
{txt}90%    {res}    -.002           .008       {txt}Variance      {res} .0014026
{txt}95%    {res}    -.002           .008       {txt}Skewness      {res} 28.93757
{txt}99%    {res}        0          1.555       {txt}Kurtosis      {res} 1231.873

                          {txt}f2_975_m4
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}     .001              0
{txt}10%    {res}     .001              0       {txt}Obs         {res}      2,476
{txt}25%    {res}     .001              0       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     .002                      {txt}Mean          {res} .0094523
                        {txt}Largest       Std. dev.     {res} .0456817
{txt}75%    {res}     .003           .118
{txt}90%    {res}      .01           .149       {txt}Variance      {res} .0020868
{txt}95%    {res}     .068           .174       {txt}Skewness      {res} 35.71802
{txt}99%    {res}     .087          2.039       {txt}Kurtosis      {res} 1575.312
{txt}
{com}. *
. xtsum  f1_mean_m4 f1_median_m4 f1_sd_m4 f1_25_m4  f1_975_m4      f2_mean_m4 f2_median_m4 f2_sd_m4 f2_25_m4 f2_975_m4 if f1_median_m3!=.

{txt}Variable         {c |}      Mean   Std. dev.       Min        Max {c |}    Observations
{hline 17}{c +}{hline 44}{c +}{hline 16}
f1_mea~4{col 10}overall {c |} {res} .0000594   .2018604      -.882       .616{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1445343      -.455   .2935789{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1456247  -.7822564   .6731646{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_med~4{col 10}overall {c |} {res} .0001527   .2018218      -.883        .62{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1444501      -.458   .2931053{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1457199  -.7843737   .6747842{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_sd_m4{col 10}overall {c |} {res} .0868768   .0466881        .04       .248{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .0331278       .048     .22425{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .0337254  -.0211232   .2408768{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_25_m4{col 10}overall {c |} {res}-.1712407   .2220574     -1.058       .472{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1558258      -.547      .1548{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1615264  -.8736618   .5526014{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_975~4{col 10}overall {c |} {res} .1693554   .2213943        -.7       .761{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1610945      -.355   .4845263{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1583354  -.6879077   .8253554{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f2_mea~4{col 10}overall {c |} {res} .0000315   .0374869      -.034      1.857{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .0085227  -.0036316   .0972105{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .0364781   -.099179   1.759821{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f2_med~4{col 10}overall {c |} {res} .0001684   .0384301      -.033      1.904{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .0087434  -.0027895   .1000526{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .0373941  -.1028842   1.804116{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f2_sd_m4{col 10}overall {c |} {res} .0047403   .0106465       .001       .139{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .0026144   .0013333   .0168947{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .0103217  -.0101544   .1268456{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f2_25_m4{col 10}overall {c |} {res}-.0094584   .0374516      -.183      1.555{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .0080311  -.0229444   .0619474{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .0365706   -.169514   1.483594{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f2_975~4{col 10}overall {c |} {res} .0094523   .0456817          0      2.039{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .0109152   .0017222   .1243684{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res}  .044328  -.1129161   1.924084{txt} {c |} T-bar = 18.3407

{com}. *
. *
. *
. sum    f1_mean_m5 f1_median_m5 f1_sd_m5 f1_25_m5  f1_975_m5      f2_mean_m5 f2_median_m5 f2_sd_m5 f2_25_m5 f2_975_m5 if f1_median_m3!=., detail

                         {txt}f1_mean_m5
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.563          -.881
{txt} 5%    {res}    -.355          -.822
{txt}10%    {res}    -.245            -.8       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.116          -.799       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}        0                      {txt}Mean          {res} .0002165
                        {txt}Largest       Std. dev.     {res} .2023715
{txt}75%    {res}    .1235           .552
{txt}90%    {res}     .257           .557       {txt}Variance      {res} .0409542
{txt}95%    {res}     .335           .584       {txt}Skewness      {res}-.3091299
{txt}99%    {res}     .463           .612       {txt}Kurtosis      {res} 3.848838

                        {txt}f1_median_m5
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.564          -.882
{txt} 5%    {res}    -.357          -.824
{txt}10%    {res}    -.246          -.799       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.117          -.798       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    .0005                      {txt}Mean          {res}  .000233
                        {txt}Largest       Std. dev.     {res} .2023909
{txt}75%    {res}     .124            .55
{txt}90%    {res}     .257           .557       {txt}Variance      {res} .0409621
{txt}95%    {res}     .336           .585       {txt}Skewness      {res}-.3090505
{txt}99%    {res}     .463           .612       {txt}Kurtosis      {res} 3.847023

                          {txt}f1_sd_m5
{hline 61}
      Percentiles      Smallest
 1%    {res}     .041           .038
{txt} 5%    {res}     .048           .039
{txt}10%    {res}     .051           .039       {txt}Obs         {res}      2,476
{txt}25%    {res}     .064           .039       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     .068                      {txt}Mean          {res} .0850691
                        {txt}Largest       Std. dev.     {res} .0456864
{txt}75%    {res}     .087           .234
{txt}90%    {res}     .132           .236       {txt}Variance      {res} .0020873
{txt}95%    {res}     .222           .236       {txt}Skewness      {res} 2.307785
{txt}99%    {res}     .231           .237       {txt}Kurtosis      {res} 7.260409

                          {txt}f1_25_m5
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.731         -1.059
{txt} 5%    {res}    -.516          -.994
{txt}10%    {res}    -.458           -.98       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.318          -.928       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    -.156                      {txt}Mean          {res}-.1675158
                        {txt}Largest       Std. dev.     {res} .2229476
{txt}75%    {res}   -.0155           .414
{txt}90%    {res}     .111           .422       {txt}Variance      {res} .0497056
{txt}95%    {res}     .191           .431       {txt}Skewness      {res}-.1990496
{txt}99%    {res}     .315           .464       {txt}Kurtosis      {res} 3.011332

                          {txt}f1_975_m5
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.397          -.705
{txt} 5%    {res}    -.219          -.678
{txt}10%    {res}    -.103          -.645       {txt}Obs         {res}      2,476
{txt}25%    {res}     .022          -.625       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     .162                      {txt}Mean          {res} .1661753
                        {txt}Largest       Std. dev.     {res} .2198512
{txt}75%    {res}     .334           .704
{txt}90%    {res}     .442           .709       {txt}Variance      {res} .0483346
{txt}95%    {res}     .492           .752       {txt}Skewness      {res}  -.32057
{txt}99%    {res}     .619            .76       {txt}Kurtosis      {res}  3.14464

                         {txt}f2_mean_m5
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.631         -1.271
{txt} 5%    {res}    -.383         -1.071
{txt}10%    {res}     -.28         -1.041       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.121          -.973       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    -.002                      {txt}Mean          {res} .0003352
                        {txt}Largest       Std. dev.     {res} .2316826
{txt}75%    {res}     .128           .919
{txt}90%    {res}     .277           .947       {txt}Variance      {res} .0536768
{txt}95%    {res}     .368           .949       {txt}Skewness      {res} -.059195
{txt}99%    {res}     .643          1.058       {txt}Kurtosis      {res} 5.110115

                        {txt}f2_median_m5
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.622         -1.266
{txt} 5%    {res}    -.381         -1.063
{txt}10%    {res}    -.279         -1.038       {txt}Obs         {res}      2,476
{txt}25%    {res}     -.12          -.971       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    -.001                      {txt}Mean          {res} .0003393
                        {txt}Largest       Std. dev.     {res} .2313833
{txt}75%    {res}     .128           .916
{txt}90%    {res}      .28           .941       {txt}Variance      {res} .0535382
{txt}95%    {res}     .371           .954       {txt}Skewness      {res}-.0590266
{txt}99%    {res}     .638          1.058       {txt}Kurtosis      {res} 5.110047

                          {txt}f2_sd_m5
{hline 61}
      Percentiles      Smallest
 1%    {res}     .142           .139
{txt} 5%    {res}     .146           .139
{txt}10%    {res}     .148            .14       {txt}Obs         {res}      2,476
{txt}25%    {res}     .168            .14       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     .317                      {txt}Mean          {res} .2698425
                        {txt}Largest       Std. dev.     {res} .0811911
{txt}75%    {res}     .325           .382
{txt}90%    {res}     .335           .382       {txt}Variance      {res}  .006592
{txt}95%    {res}     .362           .383       {txt}Skewness      {res} -.647523
{txt}99%    {res}     .375           .391       {txt}Kurtosis      {res} 1.611577

                          {txt}f2_25_m5
{hline 61}
      Percentiles      Smallest
 1%    {res}   -1.126         -1.689
{txt} 5%    {res}    -.935         -1.478
{txt}10%    {res}    -.825         -1.477       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.723         -1.406       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    -.586                      {txt}Mean          {res}-.5312876
                        {txt}Largest       Std. dev.     {res}  .293899
{txt}75%    {res}    -.386           .632
{txt}90%    {res}    -.124            .64       {txt}Variance      {res} .0863766
{txt}95%    {res}      .03           .662       {txt}Skewness      {res} .7963145
{txt}99%    {res}      .36           .743       {txt}Kurtosis      {res} 4.248769

                          {txt}f2_975_m5
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.283          -.899
{txt} 5%    {res}    -.008          -.716
{txt}10%    {res}     .154          -.693       {txt}Obs         {res}      2,476
{txt}25%    {res}    .3885          -.623       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     .584                      {txt}Mean          {res} .5276369
                        {txt}Largest       Std. dev.     {res} .2715324
{txt}75%    {res}     .705          1.226
{txt}90%    {res}     .809          1.248       {txt}Variance      {res} .0737298
{txt}95%    {res}     .899           1.26       {txt}Skewness      {res}-.9195673
{txt}99%    {res}    1.043          1.372       {txt}Kurtosis      {res} 4.387819
{txt}
{com}. *
. xtsum  f1_mean_m5 f1_median_m5 f1_sd_m5 f1_25_m5  f1_975_m5      f2_mean_m5 f2_median_m5 f2_sd_m5 f2_25_m5 f2_975_m5 if f1_median_m3!=.

{txt}Variable         {c |}      Mean   Std. dev.       Min        Max {c |}    Observations
{hline 17}{c +}{hline 44}{c +}{hline 16}
f1_mea~5{col 10}overall {c |} {res} .0002165   .2023715      -.881       .612{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1442628      -.454   .2931053{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1465855  -.7827309   .6686375{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_med~5{col 10}overall {c |} {res}  .000233   .2023909      -.882       .612{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1442601      -.456   .2936316{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1466377  -.7832933   .6709172{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_sd_m5{col 10}overall {c |} {res} .0850691   .0456864       .038       .237{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .0330769       .049   .2253333{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .0323971  -.0270362    .238648{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_25_m5{col 10}overall {c |} {res}-.1675158   .2229476     -1.059       .464{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1559159      -.547   .1625263{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1627658  -.8744631   .5391685{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_975~5{col 10}overall {c |} {res} .1661753   .2198512      -.705        .76{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1602349      -.351   .4752105{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1569157  -.6945089   .8017016{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f2_mea~5{col 10}overall {c |} {res} .0003352   .2316826     -1.271      1.058{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1428709      -.361   .3907368{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1844923  -1.151928   .9097036{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f2_med~5{col 10}overall {c |} {res} .0003393   .2313833     -1.266      1.058{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1423952      -.363   .3899474{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1844856  -1.147187   .9088129{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f2_sd_m5{col 10}overall {c |} {res} .2698425   .0811911       .139       .391{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .0441807   .1968947   .3681667{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .0683818    .092632   .4098951{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f2_25_m5{col 10}overall {c |} {res}-.5312876    .293899     -1.689       .743{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1657334     -1.006  -.0126842{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .2456331  -1.611024   .6145545{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f2_975~5{col 10}overall {c |} {res} .5276369   .2715324      -.899      1.372{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1707846   .1836842   .9028421{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .2117745  -.7724157   1.207426{txt} {c |} T-bar = 18.3407

{com}. *
. * 
. * 
. sum    f1_mean_m6 f1_median_m6 f1_sd_m6 f1_25_m6  f1_975_m6      f2_mean_m6 f2_median_m6 f2_sd_m6 f2_25_m6 f2_975_m6 if f1_median_m3!=., detail

                         {txt}f1_mean_m6
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.568          -.881
{txt} 5%    {res}    -.356          -.822
{txt}10%    {res}    -.244          -.804       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.115            -.8       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}        0                      {txt}Mean          {res} .0001987
                        {txt}Largest       Std. dev.     {res} .2025112
{txt}75%    {res}     .123           .549
{txt}90%    {res}     .257           .558       {txt}Variance      {res} .0410108
{txt}95%    {res}     .332           .588       {txt}Skewness      {res}-.3197225
{txt}99%    {res}     .463           .615       {txt}Kurtosis      {res} 3.871114

                        {txt}f1_median_m6
{hline 61}
      Percentiles      Smallest
 1%    {res}     -.57          -.882
{txt} 5%    {res}    -.356          -.824
{txt}10%    {res}    -.244          -.802       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.115          -.799       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    .0005                      {txt}Mean          {res} .0002096
                        {txt}Largest       Std. dev.     {res} .2025165
{txt}75%    {res}    .1235           .548
{txt}90%    {res}     .257           .556       {txt}Variance      {res} .0410129
{txt}95%    {res}     .334           .589       {txt}Skewness      {res}-.3184796
{txt}99%    {res}     .462           .615       {txt}Kurtosis      {res} 3.868159

                          {txt}f1_sd_m6
{hline 61}
      Percentiles      Smallest
 1%    {res}     .041           .038
{txt} 5%    {res}     .047           .039
{txt}10%    {res}     .051           .039       {txt}Obs         {res}      2,476
{txt}25%    {res}     .064           .039       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}      .07                      {txt}Mean          {res} .0861692
                        {txt}Largest       Std. dev.     {res} .0460018
{txt}75%    {res}     .088           .235
{txt}90%    {res}     .132           .237       {txt}Variance      {res} .0021162
{txt}95%    {res}     .222           .237       {txt}Skewness      {res}  2.24519
{txt}99%    {res}     .231           .238       {txt}Kurtosis      {res} 7.028634

                          {txt}f1_25_m6
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.731         -1.063
{txt} 5%    {res}    -.517          -.997
{txt}10%    {res}    -.459          -.981       {txt}Obs         {res}      2,476
{txt}25%    {res}   -.3195          -.932       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    -.157                      {txt}Mean          {res}-.1696757
                        {txt}Largest       Std. dev.     {res} .2228172
{txt}75%    {res}    -.018           .414
{txt}90%    {res}     .105           .414       {txt}Variance      {res} .0496475
{txt}95%    {res}     .186           .433       {txt}Skewness      {res}-.2046497
{txt}99%    {res}     .311           .464       {txt}Kurtosis      {res} 3.033634

                          {txt}f1_975_m6
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.394          -.706
{txt} 5%    {res}    -.217          -.686
{txt}10%    {res}    -.104          -.643       {txt}Obs         {res}      2,476
{txt}25%    {res}     .024          -.636       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     .165                      {txt}Mean          {res} .1682904
                        {txt}Largest       Std. dev.     {res} .2207064
{txt}75%    {res}    .3385           .709
{txt}90%    {res}     .444           .716       {txt}Variance      {res} .0487113
{txt}95%    {res}     .493           .758       {txt}Skewness      {res}-.3272194
{txt}99%    {res}     .624           .766       {txt}Kurtosis      {res} 3.147411

                         {txt}f2_mean_m6
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.626         -1.272
{txt} 5%    {res}    -.388         -1.078
{txt}10%    {res}    -.285         -1.042       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.122         -1.002       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    -.003                      {txt}Mean          {res} .0003481
                        {txt}Largest       Std. dev.     {res} .2342658
{txt}75%    {res}     .128           .934
{txt}90%    {res}     .282           .943       {txt}Variance      {res} .0548805
{txt}95%    {res}     .375           .957       {txt}Skewness      {res}-.0288238
{txt}99%    {res}     .659          1.051       {txt}Kurtosis      {res} 5.021579

                        {txt}f2_median_m6
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.626         -1.262
{txt} 5%    {res}    -.385         -1.069
{txt}10%    {res}    -.282         -1.041       {txt}Obs         {res}      2,476
{txt}25%    {res}   -.1215         -1.001       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    -.002                      {txt}Mean          {res} .0002548
                        {txt}Largest       Std. dev.     {res} .2338741
{txt}75%    {res}     .127           .936
{txt}90%    {res}     .281           .937       {txt}Variance      {res} .0546971
{txt}95%    {res}     .374           .953       {txt}Skewness      {res}-.0288383
{txt}99%    {res}     .654          1.048       {txt}Kurtosis      {res}  5.01567

                          {txt}f2_sd_m6
{hline 61}
      Percentiles      Smallest
 1%    {res}     .143            .14
{txt} 5%    {res}     .147            .14
{txt}10%    {res}     .149            .14       {txt}Obs         {res}      2,476
{txt}25%    {res}      .17           .141       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     .317                      {txt}Mean          {res} .2707092
                        {txt}Largest       Std. dev.     {res} .0812919
{txt}75%    {res}     .326           .384
{txt}90%    {res}     .336           .385       {txt}Variance      {res} .0066084
{txt}95%    {res}     .364           .385       {txt}Skewness      {res}-.6415966
{txt}99%    {res}     .378           .393       {txt}Kurtosis      {res} 1.614731

                          {txt}f2_25_m6
{hline 61}
      Percentiles      Smallest
 1%    {res}   -1.126         -1.689
{txt} 5%    {res}    -.939         -1.488
{txt}10%    {res}     -.83         -1.476       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.727         -1.416       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}   -.5875                      {txt}Mean          {res}-.5327157
                        {txt}Largest       Std. dev.     {res} .2958366
{txt}75%    {res}    -.383           .644
{txt}90%    {res}    -.133           .653       {txt}Variance      {res} .0875193
{txt}95%    {res}     .029           .668       {txt}Skewness      {res} .7975282
{txt}99%    {res}     .369           .734       {txt}Kurtosis      {res}  4.26492

                          {txt}f2_975_m6
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.258          -.891
{txt} 5%    {res}    -.005          -.724
{txt}10%    {res}     .154           -.72       {txt}Obs         {res}      2,476
{txt}25%    {res}    .3835          -.624       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     .583                      {txt}Mean          {res} .5294859
                        {txt}Largest       Std. dev.     {res} .2735806
{txt}75%    {res}     .712          1.243
{txt}90%    {res}     .815          1.248       {txt}Variance      {res} .0748463
{txt}95%    {res}     .902          1.267       {txt}Skewness      {res}-.8859766
{txt}99%    {res}    1.055          1.373       {txt}Kurtosis      {res}  4.27958
{txt}
{com}. *
. xtsum  f1_mean_m6 f1_median_m6 f1_sd_m6 f1_25_m6  f1_975_m6      f2_mean_m6 f2_median_m6 f2_sd_m6 f2_25_m6 f2_975_m6 if f1_median_m3!=.

{txt}Variable         {c |}      Mean   Std. dev.       Min        Max {c |}    Observations
{hline 17}{c +}{hline 44}{c +}{hline 16}
f1_mea~6{col 10}overall {c |} {res} .0001987   .2025112      -.881       .615{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1449078      -.457       .292{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res}  .146203  -.7829066   .6790934{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_med~6{col 10}overall {c |} {res} .0002096   .2025165      -.882       .615{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1448534      -.457   .2925263{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1462463  -.7838957   .6804728{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_sd_m6{col 10}overall {c |} {res} .0861692   .0460018       .038       .238{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .0331849       .049   .2260833{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .0327323  -.0217255   .2397482{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_25_m6{col 10}overall {c |} {res}-.1696757   .2228172     -1.063       .464{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1563269      -.551   .1608947{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res}  .162197  -.8781494   .5445348{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_975~6{col 10}overall {c |} {res} .1682904   .2207064      -.706       .766{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1611189      -.353   .4791579{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1573254  -.6969728   .8116588{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f2_mea~6{col 10}overall {c |} {res} .0003481   .2342658     -1.272      1.051{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1450829       -.37   .4088421{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1861108  -1.149283   .9072429{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f2_med~6{col 10}overall {c |} {res} .0002548   .2338741     -1.262      1.048{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res}  .144644       -.37   .4066316{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1859538  -1.139271   .9038864{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f2_sd_m6{col 10}overall {c |} {res} .2707092   .0812919        .14       .393{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .0443448   .1976316   .3708333{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .0683878   .0931829   .4111829{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f2_25_m6{col 10}overall {c |} {res}-.5327157   .2958366     -1.689       .734{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1675497     -1.012   .0047368{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .2467941  -1.609874   .6111791{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f2_975~6{col 10}overall {c |} {res} .5294859   .2735806      -.891      1.373{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1730609   .1937368   .9113158{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .2126443  -.7748826   1.210749{txt} {c |} T-bar = 18.3407

{com}. *
. *
. *
. sum    f1_mean_m7 f1_median_m7  f1_sd_m7  f1_25_m7  f1_975_m7 if f1_median_m3!=., detail

                         {txt}f1_mean_m7
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.554           -.87
{txt} 5%    {res}    -.354          -.815
{txt}10%    {res}     -.24          -.799       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.112          -.792       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}   -.0005                      {txt}Mean          {res} -.000191
                        {txt}Largest       Std. dev.     {res} .2003076
{txt}75%    {res}    .1195           .559
{txt}90%    {res}     .253           .578       {txt}Variance      {res} .0401231
{txt}95%    {res}     .334           .593       {txt}Skewness      {res} -.277955
{txt}99%    {res}      .46           .615       {txt}Kurtosis      {res}  3.87791

                        {txt}f1_median_m7
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.555          -.868
{txt} 5%    {res}    -.352          -.811
{txt}10%    {res}    -.241          -.793       {txt}Obs         {res}      2,476
{txt}25%    {res}   -.1135          -.791       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    -.001                      {txt}Mean          {res}-.0002363
                        {txt}Largest       Std. dev.     {res} .2002051
{txt}75%    {res}     .119           .559
{txt}90%    {res}     .253           .579       {txt}Variance      {res} .0400821
{txt}95%    {res}     .334           .595       {txt}Skewness      {res}-.2754599
{txt}99%    {res}     .459           .616       {txt}Kurtosis      {res} 3.867046

                          {txt}f1_sd_m7
{hline 61}
      Percentiles      Smallest
 1%    {res}     .039           .037
{txt} 5%    {res}     .044           .038
{txt}10%    {res}     .047           .038       {txt}Obs         {res}      2,476
{txt}25%    {res}     .064           .038       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    .0715                      {txt}Mean          {res} .0859556
                        {txt}Largest       Std. dev.     {res} .0457804
{txt}75%    {res}     .088            .26
{txt}90%    {res}     .135           .263       {txt}Variance      {res} .0020958
{txt}95%    {res}     .214           .268       {txt}Skewness      {res} 2.197082
{txt}99%    {res}     .237           .284       {txt}Kurtosis      {res} 7.062599

                          {txt}f1_25_m7
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.716         -1.047
{txt} 5%    {res}    -.514          -.995
{txt}10%    {res}     -.45          -.993       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.322          -.919       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     -.16                      {txt}Mean          {res}-.1693154
                        {txt}Largest       Std. dev.     {res} .2201051
{txt}75%    {res}     -.02           .429
{txt}90%    {res}     .106            .44       {txt}Variance      {res} .0484463
{txt}95%    {res}     .183           .451       {txt}Skewness      {res}-.1758469
{txt}99%    {res}     .314            .47       {txt}Kurtosis      {res} 3.085527

                          {txt}f1_975_m7
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.387            -.7
{txt} 5%    {res}    -.213          -.665
{txt}10%    {res}    -.102          -.643       {txt}Obs         {res}      2,476
{txt}25%    {res}    .0265           -.61       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    .1625                      {txt}Mean          {res} .1674903
                        {txt}Largest       Std. dev.     {res} .2192358
{txt}75%    {res}     .335           .725
{txt}90%    {res}     .438           .729       {txt}Variance      {res} .0480644
{txt}95%    {res}     .499           .759       {txt}Skewness      {res}-.2734305
{txt}99%    {res}     .627           .767       {txt}Kurtosis      {res} 3.109186
{txt}
{com}. *
. xtsum  f1_mean_m7 f1_median_m7  f1_sd_m7  f1_25_m7  f1_975_m7 if f1_median_m3!=.

{txt}Variable         {c |}      Mean   Std. dev.       Min        Max {c |}    Observations
{hline 17}{c +}{hline 44}{c +}{hline 16}
f1_mea~7{col 10}overall {c |} {res} -.000191   .2003076       -.87       .615{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1425902       -.45   .2930526{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1452349  -.7670331   .6772827{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_med~7{col 10}overall {c |} {res}-.0002363   .2002051      -.868       .616{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1422591      -.444   .2925263{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1452651  -.7650257   .6703427{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_sd_m7{col 10}overall {c |} {res} .0859556   .0457804       .037       .284{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .0328792       .045      .2255{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .0327508  -.0105707    .261745{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_25_m7{col 10}overall {c |} {res}-.1693154   .2201051     -1.047        .47{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1542726      -.547   .1687368{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1603152  -.8593154   .5553162{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_975~7{col 10}overall {c |} {res} .1674903   .2192358        -.7       .767{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1593381       -.36   .4778421{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1572237  -.6842465   .8202798{txt} {c |} T-bar = 18.3407

{com}. *
. *
. *
. sum    f1_mean_m8 f1_median_m8  f1_sd_m8  f1_25_m8  f1_975_m8 if f1_median_m3!=., detail

                         {txt}f1_mean_m8
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.559           -.87
{txt} 5%    {res}    -.354           -.81
{txt}10%    {res}    -.242          -.791       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.112          -.785       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    -.001                      {txt}Mean          {res}  .000246
                        {txt}Largest       Std. dev.     {res} .2000505
{txt}75%    {res}    .1215           .552
{txt}90%    {res}     .254           .577       {txt}Variance      {res} .0400202
{txt}95%    {res}     .335            .59       {txt}Skewness      {res}-.2713221
{txt}99%    {res}     .465           .611       {txt}Kurtosis      {res} 3.841235

                        {txt}f1_median_m8
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.557          -.871
{txt} 5%    {res}    -.353          -.812
{txt}10%    {res}    -.241          -.791       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.112          -.783       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}    -.002                      {txt}Mean          {res} .0002468
                        {txt}Largest       Std. dev.     {res} .2000513
{txt}75%    {res}    .1215           .552
{txt}90%    {res}     .253           .573       {txt}Variance      {res} .0400205
{txt}95%    {res}     .335           .591       {txt}Skewness      {res} -.270305
{txt}99%    {res}     .467           .613       {txt}Kurtosis      {res} 3.841452

                          {txt}f1_sd_m8
{hline 61}
      Percentiles      Smallest
 1%    {res}      .04           .038
{txt} 5%    {res}     .044           .038
{txt}10%    {res}     .047           .038       {txt}Obs         {res}      2,476
{txt}25%    {res}     .065           .039       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     .071                      {txt}Mean          {res} .0863061
                        {txt}Largest       Std. dev.     {res} .0458374
{txt}75%    {res}     .089           .232
{txt}90%    {res}     .133           .233       {txt}Variance      {res} .0021011
{txt}95%    {res}      .22           .233       {txt}Skewness      {res} 2.177218
{txt}99%    {res}     .228           .235       {txt}Kurtosis      {res} 6.841021

                          {txt}f1_25_m8
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.723         -1.049
{txt} 5%    {res}    -.513          -.999
{txt}10%    {res}    -.457          -.976       {txt}Obs         {res}      2,476
{txt}25%    {res}    -.322          -.916       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}   -.1605                      {txt}Mean          {res} -.169895
                        {txt}Largest       Std. dev.     {res} .2200495
{txt}75%    {res}    -.022           .426
{txt}90%    {res}     .109           .426       {txt}Variance      {res} .0484218
{txt}95%    {res}     .181           .445       {txt}Skewness      {res}-.1744856
{txt}99%    {res}     .311           .455       {txt}Kurtosis      {res} 3.026394

                          {txt}f1_975_m8
{hline 61}
      Percentiles      Smallest
 1%    {res}    -.384          -.702
{txt} 5%    {res}     -.21           -.66
{txt}10%    {res}      -.1          -.625       {txt}Obs         {res}      2,476
{txt}25%    {res}     .028          -.605       {txt}Sum of wgt. {res}      2,476

{txt}50%    {res}     .163                      {txt}Mean          {res} .1685969
                        {txt}Largest       Std. dev.     {res} .2187086
{txt}75%    {res}     .334           .728
{txt}90%    {res}     .443            .73       {txt}Variance      {res} .0478335
{txt}95%    {res}     .495           .756       {txt}Skewness      {res}-.2673371
{txt}99%    {res}     .626           .759       {txt}Kurtosis      {res} 3.078015
{txt}
{com}. *
. xtsum  f1_mean_m8 f1_median_m8  f1_sd_m8  f1_25_m8  f1_975_m8 if f1_median_m3!=.

{txt}Variable         {c |}      Mean   Std. dev.       Min        Max {c |}    Observations
{hline 17}{c +}{hline 44}{c +}{hline 16}
f1_mea~8{col 10}overall {c |} {res}  .000246   .2000505       -.87       .611{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1427173      -.455   .2934737{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res}   .14489  -.7717014   .6790354{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_med~8{col 10}overall {c |} {res} .0002468   .2000513      -.871       .613{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1427086      -.456   .2926316{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1449297  -.7725953   .6784573{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_sd_m8{col 10}overall {c |} {res} .0863061   .0458374       .038       .235{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .0331842       .046   .2230556{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .0324773  -.0093254   .2355693{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_25_m8{col 10}overall {c |} {res} -.169895   .2200495     -1.049       .455{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1542558      -.539   .1663684{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1601138  -.8614213   .5544208{txt} {c |} T-bar = 18.3407
{col 18}{c |}{col 63}{c |}
f1_975~8{col 10}overall {c |} {res} .1685969   .2187086      -.702       .759{txt} {c |}{col 69}N =    2476
{col 10}between {c |}{col 31}{res} .1593859      -.362   .4778947{txt} {c |}{col 69}n =     135
{col 10}within  {c |}{col 31}{res} .1564055  -.6921925   .8043338{txt} {c |} T-bar = 18.3407

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********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************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. **** STEP 4: COMPUTE BIVARIATE CORRELATIONS FROM THE OMNIBUS DATABASE [MODEL 1 - MODEL 8] OF FACTOR SCORE ESTIMATES OF THE POSTERIOR MEDIAN & STANDARD DEVIATION ****
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. *** FACTOR SCORE ESTIMATES OF POSTERIOR MEDIAN BIVARIATE CORRELATIONS ***
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. correlate f1_median_m1  f1_median_m2  f1_median_m3  f1_median_m4  f1_median_m5  f1_median_m6  f1_median_m7  f1_median_m8      f2_median_m2   f2_median_m4    f2_median_m5   f2_median_m6 
{txt}(obs=2,476)

             {c |} f1_med~1 f1_med~2 f1_med~3 f1_med~4 f1_med~5 f1_med~6 f1_med~7 f1_med~8 f2_med~2 f2_med~4 f2_med~5 f2_med~6
{hline 13}{c +}{hline 108}
f1_median_m1 {c |}{res}   1.0000
{txt}f1_median_m2 {c |}{res}   0.9995   1.0000
{txt}f1_median_m3 {c |}{res}   0.9891   0.9892   1.0000
{txt}f1_median_m4 {c |}{res}   0.9890   0.9891   0.9995   1.0000
{txt}f1_median_m5 {c |}{res}   0.9951   0.9952   0.9969   0.9968   1.0000
{txt}f1_median_m6 {c |}{res}   0.9943   0.9943   0.9982   0.9981   0.9994   1.0000
{txt}f1_median_m7 {c |}{res}   0.9963   0.9965   0.9859   0.9861   0.9911   0.9903   1.0000
{txt}f1_median_m8 {c |}{res}   0.9995   0.9995   0.9891   0.9889   0.9950   0.9942   0.9963   1.0000
{txt}f2_median_m2 {c |}{res}   0.0172   0.0250   0.0184   0.0237   0.0187   0.0182   0.0222   0.0184   1.0000
{txt}f2_median_m4 {c |}{res}   0.0177   0.0254   0.0190   0.0247   0.0194   0.0190   0.0223   0.0187   0.9931   1.0000
{txt}f2_median_m5 {c |}{res}   0.7803   0.7794   0.7161   0.7153   0.7441   0.7408   0.7656   0.7800   0.0105   0.0098   1.0000
{txt}f2_median_m6 {c |}{res}   0.7914   0.7905   0.7271   0.7262   0.7562   0.7515   0.7768   0.7911   0.0108   0.0100   0.9974   1.0000

{txt}
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. *** FACTOR SCORE ESTIMATES OF POSTERIOR STANDARD DEVIATION BIVARIATE CORRELATIONS ***
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. correlate f1_sd_m1  f1_sd_m2  f1_sd_m3  f1_sd_m4  f1_sd_m5  f1_sd_m6  f1_sd_m7  f1_sd_m8      f2_sd_m2   f2_sd_m4    f2_sd_m5   f2_sd_m6 
{txt}(obs=2,476)

             {c |} f1_sd_m1 f1_sd_m2 f1_sd_m3 f1_sd_m4 f1_sd_m5 f1_sd_m6 f1_sd_m7 f1_sd_m8 f2_sd_m2 f2_sd_m4 f2_sd_m5 f2_sd_m6
{hline 13}{c +}{hline 108}
    f1_sd_m1 {c |}{res}   1.0000
    {txt}f1_sd_m2 {c |}{res}   0.9973   1.0000
    {txt}f1_sd_m3 {c |}{res}   0.9887   0.9889   1.0000
    {txt}f1_sd_m4 {c |}{res}   0.9888   0.9891   0.9974   1.0000
    {txt}f1_sd_m5 {c |}{res}   0.9924   0.9926   0.9945   0.9943   1.0000
    {txt}f1_sd_m6 {c |}{res}   0.9916   0.9918   0.9962   0.9960   0.9991   1.0000
    {txt}f1_sd_m7 {c |}{res}   0.9916   0.9920   0.9828   0.9828   0.9856   0.9850   1.0000
    {txt}f1_sd_m8 {c |}{res}   0.9972   0.9976   0.9888   0.9887   0.9925   0.9918   0.9917   1.0000
    {txt}f2_sd_m2 {c |}{res}   0.1726   0.1727   0.1578   0.1601   0.1532   0.1596   0.1710   0.1707   1.0000
    {txt}f2_sd_m4 {c |}{res}   0.1713   0.1732   0.1574   0.1605   0.1525   0.1590   0.1698   0.1688   0.9693   1.0000
    {txt}f2_sd_m5 {c |}{res}   0.2747   0.2772   0.2278   0.2248   0.2650   0.2488   0.2701   0.2771  -0.0578  -0.0528   1.0000
    {txt}f2_sd_m6 {c |}{res}   0.2796   0.2822   0.2327   0.2297   0.2700   0.2538   0.2749   0.2820  -0.0567  -0.0518   0.9999   1.0000

{txt}
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********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************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. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\gk57526\Dropbox\AP_Mplus\Mplus Output\BSEM (August 2025)\bpsumstats_conversion.08-08-2025.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 8 Aug 2025, 14:56:47
{txt}{.-}
{smcl}
{txt}{sf}{ul off}